Autonomous Vehicle Intent Prediction via Segmented Machine Learning
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the behavior of objects in their environment, particularly when the behavior changes in response to the vehicle's actions, requiring advanced models to anticipate future intentions of pedestrians and other objects for safe navigation.
Innovation Solution
Implementing machine learned models that process trajectory data, weights, and map information to predict the intentions of objects, such as pedestrians, by conditioning on the vehicle's actions, allowing for the consideration of multiple possible trajectories and intentions, and distinguishing between road and freeform trajectory types to improve planning and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learned models are used to predict object behavior and intent, then prediction accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The prediction system is divided into multiple specialized machine learned models, each handling specific aspects of behavior prediction. The system segments prediction into trajectory forecasting, intent classification, and interaction response modeling, allowing each component to be optimized independently while maintaining overall accuracy without excessive complexity
Solution Approach 2:
The system performs preliminary processing of sensor data and environmental information before feeding it to the machine learned models. By pre-processing and structuring input data in advance, the models receive optimized inputs that reduce computational burden during real-time prediction while maintaining high accuracy
2Reliability
If multiple trajectories and intentions are considered, then safety improves, but processing time and computational resources increase
Solution Approach 1:
The system considers multiple trajectories and intentions but applies prioritization to focus computational resources on the most likely and safest scenarios. Rather than exhaustively evaluating all possible trajectories, the system identifies and deeply analyzes the top N most probable trajectories, providing sufficient safety assurance with reduced processing time
Solution Approach 2:
The system uses feedback from ongoing predictions and actual object behavior to dynamically adjust the number of trajectories analyzed. When objects exhibit predictable behavior, fewer trajectories are evaluated; when uncertainty increases, the system automatically increases analysis depth, optimizing the balance between safety and processing time
3Reliability
If behavior prediction accounts for vehicle actions, then navigation safety improves, but model complexity increases
Solution Approach 1:
An intermediary planning module sits between the vehicle's action selection and the behavior prediction models. This intermediary translates high-level vehicle intentions into detailed action parameters that the prediction models can process, simplifying the coupling between navigation decisions and behavior prediction while maintaining safety through coordinated interaction
Data Source
AI summary
Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.